Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39050860", "kind": "science-record", "revision": 4, "content_hash": "sha256:2078c71a8b6cc85f39ffc3b6f8d9cd60dbe4dd3d2e3d353eaaf9258b06a814e2", "created_at": "2026-11-01T20:29:07Z", "updated_at": "2026-11-01T22:54:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.", "doi": "10.3389/fimmu.2024.1401957", "pmid": "39050860", "authors": ["Hao Zhang", "Shengbo Shi", "Xingxing Huang", "Changsheng Gong", "Zijing Zhang", "Zetian Zhao", "Junxiao Gao", "Meng Zhang", "Xiaobing Yu"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific mechanisms of IDD remain unclear. This study aims to identify key genes and pathways associated with IDD using bioinformatics and machine learning algorithms.\nMethods: Gene expression profiles, including those from 35 LDH patients and 43 healthy volunteers, were downloaded from the GEO database (GSE124272, GSE150408, GSE23130, GSE153761). After merging four microarray datasets, differentially expressed genes (DEGs) were selected for GO and KEGG pathway enrichment analysis. Weighted Gene Co-expression Network Analysis (WGCNA) was then applied to the merged dataset to identify relevant modules and intersect with DEGs to discover candidate genes with diagnostic value. A LASSO model was established to select appropriate genes, and ROC curves were drawn to elucidate the diagnostic value of genetic markers. A Protein-Protein Interaction (PPI) network was constructed and visualized to determine central genes, followed by external validation using qRT-PCR.\nResults: Differential analysis of the preprocessed dataset identified 244 genes, including 183 upregulated and 61 downregulated genes. WGCNA analysis revealed the most relevant module intersecting with DEGs, yielding 9 candidate genes. The lasso-cox method was used for regression analysis, ultimately identifying 6 genes: ASPH, CDC42EP3, FOSL2, IL1R1, NFKBIZ, TCF7L2. A Protein-Protein Interaction (PPI) network created with GENEMANIA identified IL1R1 and TCF7L2 as central genes.\nConclusion: Our study shows that IL1R1 and TCF7L2 are the core genes of IDD, offering new insights into the pathogenesis and therapeutic development of IDD.", "claims": [{"id": "claim-method", "statement": "Differential analysis was conducted using the Limma R package, with |log2FC| > 1.3 and p < 0.05 set as the criteria for selecting differentially expressed genes (DEGs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39050860", "locator": "sections[4] \"Selection of differentially expressed genes\" paragraph 2", "quote": "Differential analysis was conducted using the Limma R package, with |log2FC| > 1.3 and p < 0.05 set as the criteria for selecting differentially expressed genes (DEGs)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39050860", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Six characteristic genes were identified by lasso-cox regression, and IL1R1 and TCF7L2 were subsequently identified as the core genes.", "kind": "quantitative", "target": {"value": 5.0, "unit": "fold change", "tolerance": null, "source": "A 5-fold cross-validation was utilized to determine the optimal model, selecting a Lambda value of 0.0462682649698763, ultimately identifying 6 genes: ASPH, CDC42EP3, FOSL2, IL1R1, NFKBIZ, TCF7L2 (see Figures 4A, B )."}, "evidence": [{"asset": "source:pmid:39050860", "locator": "sections[17] \"Identification of characteristic genes and application of machine learning algorithms\" paragraph 2", "quote": "A 5-fold cross-validation was utilized to determine the optimal model, selecting a Lambda value of 0.0462682649698763, ultimately identifying 6 genes: ASPH, CDC42EP3, FOSL2, IL1R1, NFKBIZ, TCF7L2 (see Figures 4A, B )."}, {"asset": "source:pmid:39050860", "locator": "sections[18] \"Construction of PPI networks to identify core genes\" paragraph 2", "quote": "Through a comprehensive analysis of the PPI network, IL1R1 and TCF7L2 were identified as the final core genes of this study."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Six characteristic genes were identified by lasso-cox regression, and IL1R1 and TCF7L2 were subsequently identified as the core genes.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39050860"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE124272"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE124272"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39050860", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1401957", "sha256": "sha256:2b82a6fb004ef9ff43722ef84900415b47af29ab50b9c76cb5b90ce5d7dff3a2", "bytes": 26554, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1401957", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11266004", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE124272", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124272", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE124272", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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